DevOps, security, and SOC teams find themselves constantly facing new cyber threats, ever-evolving attackers, and innovative attack vectors. Their challenges range from protecting employees’ mobile devices to preventing malicious parties from accessing an organization’s financial data or customers’ personal information.
Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money. Less than a year later, Loki has almost 6,500 stars on GitHub and is now quickly approaching GA.
So you have been adding more and more logs to your Graylog instance, gathering up your server, network, application logs and throwing in anything else you can think of. This is exactly what Graylog is designed for, to collect all the logs and have them ready for you to search through in one place. Unfortunately, during your administration of Graylog you go the System -> Overview screen and see the big bad red box, saying you are having indexing failures.
Continuous integration and continuous deployment (CI/CD) has enabled teams to build and deploy software at a much faster pace. DevOps teams can build, test, and deploy changes to production in a matter of minutes, allowing for extremely rapid release cycles. However, a CI/CD pipeline has a lot of moving parts and steps where problems to occur. In order to ensure a successful deployment, it’s important to monitor each step in this process.
I have been super fortunate to work with incredibly innovative, talented teams that create powerful technology to help manage the world's data. When I met with Thomas Hazel, CHAOSSEARCH founder and CTO, and Les Yetton, CHAOSSEARCH co-founder and CEO to talk about CHAOSSEARCH — scalable, performant text search to your object storage without having to move any data — I knew it was special and was eager to join.
The next major version of the LogDNA agent is right around the corner, and we’re introducing some significant improvements. Most notably, we’ve completely rewritten the agent using Rust instead of Node.js. As we gear up for the release, we wanted to explain why we chose Rust for the LogDNA agent, the benefits that it offers over other languages, and how it can help you log faster. Rust is a systems programming language that aims to offer both performance and safety.
One of the benefits of using Logstash in your data pipelines is the ability to transform the data into the desired format according to the needs of your system and organization. There are many ways of transforming data in Logstash, one of them is using the mutate filter plugin. This Logstash filter plugin allows you to force fields into specific data types and add, copy, and update specific fields to make them compatible across the environment.
In the previous post, we discussed the various environments that Log360 helps you audit and secure. Having established the ease of Log360’s use and the breadth of its auditing scope, now we’ll examine some of the critical areas it can help you monitor. With over 1,000 predefined reports and alerts for several crucial types of network activity, Log360 provides comprehensive network auditing.
The use cases and requirements of a logging platform in an organization varies between teams and job functions. The problem isn’t in collecting log data (we are a logging company after all), but in deciding how to manage these logs for each team. For example, our backend developers need detailed, short-lived logs in order to build and test new features; while our infrastructure team needs lengthy retention periods for auditing and compliance.
Looking to learn about Logstash as quickly as possible? This Logstash Tutorial is for you: we’ll install Logstash and push some Apache logs to Elasticsearch in less than 5 minutes. Logstash is a good (if not the) swiss-army knife for logs. It works by reading data from many sources, processing it in various ways, then sending it to one or more destinations, the most popular one being Elasticsearch.
